Abstract

Background

Association testing is a powerful tool for identifying disease susceptibility genes
underlying complex diseases. Technological advances have yielded a dramatic increase
in the density of available genetic markers, necessitating an increase in the number
of association tests required for the analysis of disease susceptibility genes. As
such, multiple-tests corrections have become a critical issue. However the conventional
statistical corrections on locus-specific multiple tests usually result in lower power
as the number of markers increases. Alternatively, we propose here the application
of the longest significant run (LSR) method to estimate a region-specific p-value to provide an index for the most likely
candidate region.

Results

An advantage of the LSR method relative to procedures based on genotypic data is that only p-value data are
needed and hence can be applied extensively to different study designs. In this study
the proposed LSR method was compared with commonly used methods such as Bonferroni's method and FDR
controlling method. We found that while all methods provide good control over false
positive rate, LSR has much better power and false discovery rate. In the authentic analysis on psoriasis
and asthma disease data, the LSR method successfully identified important candidate regions and replicated the results
of previous association studies.

Conclusion

The proposed LSR method provides an efficient exploratory tool for the analysis of sequences of dense
genetic markers. Our results show that the LSR method has better power and lower false discovery rate comparing with the locus-specific
multiple tests.